Power Hungry Processing: Watts Driving the Cost of AI Deployment?

Author:

Luccioni Sasha1ORCID,Jernite Yacine2ORCID,Strubell Emma3ORCID

Affiliation:

1. Hugging Face, Canada

2. Hugging Face, United States

3. Carnegie Mellon, Allen AI Institute, USA

Publisher

ACM

Reference58 articles.

1. Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools

2. Jeff Barr. 2019. Amazon ec2 update–inf1 instances with AWS inferentia chips for high performance cost-effective inferencing. https://aws.amazon.com/blogs/aws/amazon-ec2-update-inf1-instances-with-aws-inferentia-chips-for-high-performance-cost-effective-inferencing/

3. Bing. 2019. Bing delivers its largest improvement in search experience using Azure GPUs. https://azure.microsoft.com/en-us/blog/bing-delivers-its-largest-improvement-in-search-experience-using-azure-gpus/

4. Bing. 2023. Confirmed: the new Bing runs on OpenAI’s GPT-4. https://blogs.bing.com/search/march_2023/Confirmed-the-new-Bing-runs-on-OpenAI%E2%80%99s-GPT-4

5. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.

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